Cart inspection for suspicious items

ABSTRACT

Methods and apparatus provide for a Cart Inspector to create a suspicion level for a transaction when a video image of the transaction portrays an item(s) left in a shopping cart. Specifically, the Cart Inspector obtains video data associated with a time(s) of interest. The video data originates from a video camera that monitors a transaction area. The Cart Inspector analyzes the video data with respect to target image(s) associated with a transaction in the transaction area during the time(s) of interest. The Cart Inspector creates an indication of a suspicion level for the transaction based on analysis of the target image(s). Creation of a high suspicion level for the transaction indicates that the transaction&#39;s corresponding video images most likely portray occurrences where the purchase price of an item transported through the transaction area was not included in the total amount paid by the customer.

PRIORITY TO PROVISIONAL APPLICATION

This U.S. Utility Patent Application claims the benefit of the filingdate of earlier filed U.S. Provisional Application for patent havingU.S. Ser. No. 60/906,692, filed on Mar. 12, 2007, entitled “Methods andApparatus for Cart Inspection for Suspicious Items.” The entireteaching, disclosure and contents of this provisional patent are herebyincorporated by reference herein in their entirety.

BACKGROUND

Retail establishments commonly utilize point of sale or othertransaction terminals, often referred to as cash registers, to allowcustomers of those establishments to purchase items. As an example, in aconventional department store, supermarket or other retailestablishment, a customer collects items for purchase throughout thestore and places them in a shopping cart, basket, or simply carries themto a point of sale terminal to purchase those items in a transaction.The point of sale terminal may be staffed with an operator such as acashier who is a person employed by the store to assist the customer incompleting the transaction. In some cases, retail establishments haveimplemented self-checkout point of sale terminals in which the customeris the operator.

In either case, the operator typically places items for purchase on acounter, conveyor belt or other item input area. The point of saleterminals include a scanning device such as a laser or optical scannerdevice that operates to identify a Uniform Product Code (UPC) label orbar code affixed to each item that the customer desires to purchase. Thelaser scanner is usually a peripheral device coupled to a computer thatis part of the POS terminal.

To scan an item, the operator picks up each item, one by one, from theitem input area and passes that item over a scanning area such as glasswindow built into the counter or checkout area to allow the laserscanner to detect the UPC code. Once the point of sale computeridentifies the UPC code on an item, the computer can perform a lookup ina database to determine the price and identity of the scanned item.Alternatively, in every case where the operator can scan the item, theoperator may likewise enter the UPC or product identification code intothe terminal manually or through an automatic product identificationdevice such as an RFID reader. The term “scan” is defined generally toinclude all means of entering transaction items into a transactionterminal. Likewise, the term “scanner” is defined generally as anytransaction terminal, automated and/or manual, for recording transactioninformation.

As the operator scans or enters each item for purchase, one by one, thepoint of sale terminal maintains an accumulated total purchase price forall of the items in the transaction. For each item that an operatorsuccessfully scans or enters, the point of sale terminal typically makesa beeping noise or tone to indicate to the operator that the item hasbeen scanned by the point of sale terminal and in response, the operatorplaces the item into an item output area such as a conveyor belt orother area for retrieval of the items by the customer or for bagging ofthe items into a shopping bag. Once all items in the transaction arescanned in this manner, the operator indicates to the point of saleterminal that the scanning process is complete and the point of saleterminal displays a total purchase price to the customer who then paysthe store for the items purchased in that transaction.

SUMMARY

Conventional transaction terminal systems suffer from a variety ofdeficiencies. In particular, if an item is intentionally or accidentallynot placed on the conveyor belt, conventional transaction terminalsystems rely on the operator to notice the omitted item as the customerpasses by the point of sale terminal. Upon noticing the omitted item, itis the operator's responsibility to scan the omitted item so that theomitted item's purchase price is added to the total amount to be paid bythe customer. Thus, although the omitted item was never placed on theconveyor belt, the total amount paid by the customer should be anaggregate of the omitted item's price and the price of each item thatwas placed on the conveyor belt and scanned by the operator.

For example, when the customer empties items from a shopping cart ontothe conveyor belt, the customer may keep an item in the shopping cart sothat it will not be scanned. As the customer passes by the point of saleterminal, if the operator fails to notice the item left in the shoppingcart, or willfully ignores the item left in the shopping cart, then theitem left in the shopping cart is never scanned. Hence, the price of theitem left in the shopping cart is not included in the total amount thatthe customer pays. The customer thereby avoids having to pay for theitem left in the shopping cart, effectively receiving the item for free,which results in a financial loss to the retail establishment.

To detect the occurrence of such events, current retail establishmentsemploy video security cameras to record operator behavior near the pointof sale terminal. By recording the operator's behavior, securitypersonnel can review video to determine whether or not the operator isfailing to scan items left in shopping carts. However, such an approachis burdensome because security personnel are forced to search throughevery frame of video in order to find instances when the operator failedto scan items left in shopping carts. This approach is time intensiveand requires that security personnel be highly alert when reviewing amultitude of somewhat repetitive and similar video images.

Other conventional systems provide camera systems installed below (or inthe side of) retail checkout counters to specifically capture videoimages of items stored beneath shopping cart carriages as shopping cartsmove near a point-of-sale terminal. These conventional systems aredeficient because retailers are forced to install these camera systemseven though the retailers most likely already have security videosystems in place. Thus, retailers are burdened by having to invest inand concurrently maintain two separate camera systems. Anotherdeficiency is that the captured video images only portray those itemssituated in an undercarriage portion of the shopping cart. Thus, if anitem involved in the transaction is placed in the shopping cart's mainbasket, then the captured video images are of no use because such anitem would not have been recorded by the camera system mounted below theretail checkout counter.

Techniques discussed herein significantly overcome the deficiencies ofconventional applications such as those discussed above as well asadditional techniques also known in the prior art. As will be discussedfurther, certain specific embodiments herein are directed to a CartInspector. The one or more embodiments of the Cart Inspector asdescribed herein contrast with conventional systems by performing videoanalysis of images that portray a transaction near a point of saleterminal. Based on the video analysis, a suspicion level for thetransaction is created when the image of the transaction portrays anitem(s) left in the shopping cart at a particular time during thetransaction. Retailers that already employ security video systems canuse the Cart Inspector instead of installing video cameras specificallyfor the Cart Inspector. Thus, retail environments can enhance theirsecurity video systems by having their video data processed by the CartInspector.

In general embodiments, transactions given a high suspicion level mostlikely have corresponding video images which portray occurrences wherethe purchase price of an item left in a shopping cart (i.e. transportedthrough a transaction area) was not included in the total amount paid bythe customer. By knowing which transactions have high suspicion levels,security personnel can efficiently locate the corresponding video imagesfor further review. Thus, the Cart Inspector results in a decrease ofvideo image search costs for security personnel.

The Cart Inspector analyzes video data with respect to a target imageassociated with a transaction in the transaction area during a time ofinterest. Based on analysis of the target image, the Cart Inspectorcreates an indication of a suspicion level for the transaction. It isunderstood that the target image can be a direct overhead view of acart, or an elevated perspective view of the cart.

In one embodiment, the target image can portray an image of a cart (e.g.a shopping cart at a critical location in the transaction area). Thecritical location is an area in the transaction area well-suited fordifferentiating between suspicious activity and non-suspicious activity.For example, the target image can portray the cart just prior to thecart exiting the retail environment that provides the transaction area.In another example, the target image can portray an image of a cart justafter the cart moves away from a point-of-sale terminal in thetransaction area. In other examples, the target image can portray animage of a cart next to (or proximate to) a point-of-sale terminal orthe target image can portray the cart at the time of interest. It isunderstood that the cart is any device suitable for transporting itemsthrough the transaction area.

In one embodiment, the Cart Inspector obtains video data by identifyinga time stamp in transaction data. By defining the time of interest ascontemporaneous with the time stamp, the Cart Inspector identifies thetarget image from a portion of the video data that was created duringthe time of interest. For example, by identifying a last time stamp inthe transaction data, the Cart Inspector can use the time represented bythe last time stamp to find video data created at that time. Thus, theCart Inspector utilizes the last time stamp as a time of interest whereit can assume that all paid-for merchandise items were most likely takenout of the cart and placed on the conveyor belt—or otherwise enteredinto the transaction manually or by hand scanner.

In another embodiment, the Cart Inspector obtains video data thatcaptured activity occurring at a critical location in the transactionarea. The time of interest is thereby defined as a moment of time inwhich the cart was present at the critical location.

In order to analyze the video data, the Cart Inspector performs acomparison of the target image with a reference representation todetermine the extent to which that target image portrays imagery ofnon-suspicious activity or imagery of suspicious activity. The referencerepresentation can be a template that represents a non-suspicioustransaction or an empty cart (e.g. an empty shopping cart). In anotherembodiment, the reference representation can be a template of anon-suspicious cart state that represents an appearance of a cart justafter the point-of-sale location in the transaction area.

If the comparison between the target image and the referencerepresentation results in detection of a similarity between the targetimage and the reference representation, the Cart Inspector sets theindication of the suspicion level for the transaction to a lowestsuspicion level.

If the comparison between the target image and the referencerepresentation results in detection of a difference between the targetimage and the reference representation, the Cart Inspector adjusts thesuspicion level of the transaction. For each portion of the target imagethat portrays an item transported through the transaction area, the CartInspector adjusts the suspicion level as a function of a variety offactors related to the item.

Factors used by the Cart Inspector to adjust the suspicion levelinclude, but are not limited to: the location of the item in the cart,the item's shape, the item's color, a characteristic of the item, and aprobability that the item qualifies for a false positive classification.Each factor thereby influences the amount the suspicion level isadjusted.

In another embodiment, for multiple transactions captured in the videodata, the Cart Inspector creates an indication of a suspicion level foreach transaction based on analysis of target images associated with eachof the multiple transactions. The Cart Inspector creates a ranking ofthe multiple transactions (or a ranking of the target images) based onthe created suspicion levels.

In another embodiment, the Cart Inspector defines a threshold suspicionlevel. The Cart Inspector compares the indication of the suspicion levelfor the transaction based on analysis of the target image with thethreshold suspicion level. Upon detecting that the indication of thesuspicion level surpasses the threshold suspicion level, the CartInspector creates a notification associated with the transaction. In oneembodiment, the Cart Inspector creates the real-time notificationcontemporaneously with the transaction as the transaction occurs in aself-checkout transaction area or an assisted checkout transaction area.

In another embodiment, the Cart Inspector compares a target image of ashopping cart in the transaction area with a reference representation(e.g. stored image of an empty shopping cart), the Cart Inspectordetermines whether the two images are similar enough to deduce that theshopping cart was most likely empty during a particular time of interestin the transaction area. If there is a similarity, a low suspicion levelis created for the transaction because the similarity between the twoimages signifies that it is highly likely that the customer placed allthe items sought to be purchased upon the conveyor belt. However, ifthere is a difference between the target image and the referencerepresentation, the Cart Inspector assigns a suspicion level to thetransaction, such as a default suspicion level.

Upon assigning the level of suspicion to the transaction, the CartInspector modifies the level of suspicion based on video analysis withrespect to an item(s) represented in the image of the shopping cart.Thus, for a target image of a shopping cart that contains a plurality ofitems located in a various compartment of the shopping cart, the CartInspector can perform video analysis with respect to each item in thetarget image to create a suspicion level for the transaction. Thesuspicion level is adjusted according to the location of each item inthe cart, the each item's shape, the each item's color, a characteristicof each item, and a probability that each item qualifies for a falsepositive classification.

Other embodiments disclosed herein include any type of computerizeddevice, workstation, handheld or laptop computer, or the like configuredwith software and/or circuitry (e.g., a processor) to process any or allof the method operations disclosed herein. In other words, acomputerized device such as a computer or a data communications deviceor any type of processor that is programmed or configured to operate asexplained herein is considered an embodiment disclosed herein.

Other embodiments disclosed herein include software programs to performthe steps and operations summarized above and disclosed in detail below.One such embodiment comprises a computer program product that has acomputer-readable medium (e.g., tangible computer-readable medium)including computer program logic encoded thereon that, when performed ina computerized device having a coupling of a memory and a processor,programs the processor to perform the operations disclosed herein. Sucharrangements are typically provided as software, code and/or other data(e.g., data structures) arranged or encoded on a computer readablemedium such as an optical medium (e.g., CD-ROM), floppy or hard disk orother a medium such as firmware or microcode in one or more ROM or RAMor PROM chips or as an Application Specific Integrated Circuit (ASIC).The software or firmware or other such configurations can be installedonto a computerized device to cause the computerized device to performthe techniques explained as embodiments disclosed herein.

Additionally, although each of the different features, techniques,configurations, etc. herein may be discussed in different places of thisdisclosure, it is intended that each of the concepts can be executedindependently of each other or in combination with each other.Accordingly, the present invention can be embodied and viewed in manydifferent ways.

Note also that this summary section herein does not specify everyembodiment and/or incrementally novel aspect of the present disclosureor claimed invention. Instead, this summary only provides a preliminarydiscussion of different embodiments and corresponding points of noveltyover conventional techniques. For additional details and/or possibleperspectives (permutations) of the invention, the reader is directed tothe Detailed Description section and corresponding figures of thepresent disclosure as further discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features and advantages of theinvention will be apparent from the following more particulardescription of embodiments of the methods and apparatus for a CartInspector, as illustrated in the accompanying drawings and figures inwhich like reference characters refer to the same parts throughout thedifferent views. The drawings are not necessarily to scale, withemphasis instead being placed upon illustrating the embodiments,principles and concepts of the methods and apparatus in accordance withthe invention.

FIG. 1 is an example block diagram of Cart Inspector environmentaccording to embodiments herein.

FIG. 2 is an example block diagram of a computer system configured witha Cart Inspector creating a low suspicion level for a transactionaccording to embodiments herein.

FIG. 3 is an example block diagram of a computer system configured witha Cart Inspector classifying a shopping cart item, which is portrayed inimages of a shopping cart, as a non-store item (i.e. a moving item)according to embodiments herein.

FIG. 4 is an example block diagram of a computer system configured witha Cart Inspector classifying a shopping cart item, which is portrayed inan image of a shopping cart, as a bagged item according to embodimentsherein.

FIG. 5 is an example block diagram of a computer system configured witha Cart Inspector classifying a shopping cart item as a bulk itemaccording to embodiments herein.

FIG. 6 is an example block diagram of a computer system configured witha Cart Inspector classifying a shopping cart item, which is portrayed inan image of a shopping cart, as a suspicious item according toembodiments herein.

FIG. 7 is an example block diagram illustrating an architecture of acomputer system that executes, runs, interprets, operates or otherwiseperforms a Cart Inspector application and/or Cart Inspector processaccording to embodiments herein.

FIG. 8 is an example flowchart of processing steps performed by the CartInspector to create an indication of a suspicion level for a transactionaccording to embodiments herein.

FIG. 9 is an example flowchart of processing steps performed by the CartInspector to obtain video data associated with a time of interestaccording to embodiments herein.

FIGS. 10-11 are example flowcharts of processing steps performed by theCart Inspector to compare an image of a shopping cart with a predefinedimage of an empty shopping cart according to embodiments herein.

FIG. 12 is an example flowchart of processing steps performed by theCart Inspector to determine whether a shopping cart item portrayed in animage of a shopping cart qualifies as a moving item, a bagged item, anobservable item or a bulk item according to embodiments herein.

FIG. 13 is an example flowchart of processing steps performed by theCart Inspector to create a minimum suspicion level that represents aleast suspicious state of the transaction according to embodimentsherein.

DETAILED DESCRIPTION

Methods and apparatus provide for a Cart Inspector to create a suspicionlevel for a transaction when a video image of the transaction portraysan item(s) left in a shopping cart. Specifically, the Cart Inspectorobtains video data associated with a time(s) of interest. The video dataoriginates from a video camera that monitors a transaction area. TheCart Inspector analyzes the video data with respect to target image(s)associated with a transaction in the transaction area during the time(s)of interest. The Cart Inspector creates an indication of a suspicionlevel for the transaction based on analysis of the target image(s).Creation of a high suspicion level for the transaction indicates thatthe transaction's corresponding video images most likely portrayoccurrences where the purchase price of an item transported through thetransaction area was not included in the total amount paid by thecustomer.

In one example embodiment, the Cart Inspector obtains a target image ofa shopping cart carrying a first item in its main basket and a seconditem in a compartment beneath the basket. The target image can be adirect overhead view of the shopping cart or an elevated view of theshopping cart. It is understood that the Cart Inspector can performvideo analysis on a target image of a shopping cart that contains anynumber items placed in various regions and compartments of the shoppingcart.

Upon detecting a lack of similarity between the target image and areference representation, the Cart Inspector adjusts the transaction'ssuspicion level based on the location of the first and second item inthe cart, the shape of the first and second item, the color of the firstand second item, various other characteristics of the first and seconditem, and a probability that the first and second item qualify for afalse positive classification. Each factor can concurrently decrease orincrease the suspicion level according to a predefined amount to resultin a final suspicion level of the transaction portrayed in the targetimage.

FIG. 1 is an example block diagram of Cart Inspector environment 100according to embodiments herein.

According to the workflow of the transaction area 200, when a shoppingcart 210 used during a transaction is near a point of sale terminal 230,it should either be empty or transporting bagged items, bulk items,non-store items (e.g. a child, a handbag, a flier, a pet, etc.), oritems that are clearly observable to an operator of the point of saleterminal 230. It is understood that the shopping cart 210 can be anydevice for transporting goods in the transaction area 200, for example,such as a basket or a dolly.

If the shopping cart 210 contains (or is transporting) bagged items, itis highly likely that the items in the bag were placed on a conveyorbelt, scanned by the operator of the point of sale terminal 230, andplaced in a shopping bag. The appearance of bagged items in the shoppingcart 210 thereby creates a high likelihood that the prices of thoseitems in the shopping bag are included in the total amount to be paid bythe customer. Thus, the appearance of a shopping cart 210 containingbagged items when it is in the transaction area 200 is not a suspiciousevent.

Another non-suspicious event is the appearance of a shopping cart 210transporting bulk items. Bulk items are those items that are too awkwardto ever be placed on the conveyor belt, such as a 36-pack of bottledwater. Hence, transactions that include a purchase of a bulk itemusually never involve the customer emptying the shopping cart 210because the bulk item is never place on the downward conveyor belt.Instead, it is customary for the operator to manually enter the price ofthe bulk item when the shopping cart 210 is near the point of saleterminal 230. Thus, when a shopping cart contains a bulk item, it isalso likely that the bulk item's price is included in the total amountto be paid by the customer.

When the shopping cart 210 transports a moving item (i.e. an animatedobject), such as a child or pet, when it is in the transaction area 200,the appearance of the moving item in the shopping cart 210 is anon-suspicious event as well.

However, if the shopping cart 210 is not empty when it is near the pointof sale terminal 250 and the item transported by the shopping cart 210is not a bagged item, a bulk item, or a moving item, then the item is aloose item. A loose item is an item that most likely was never placed onthe conveyor belt and scanned by the operator of the point of saleterminal 250. Hence, the appearance of the loose item in the shoppingcart 210 is a suspicious event because the loose item's transportationby the shopping cart 210 indicates that the loose item's price may notbe included in the total amount to be paid by the customer.

In order to capture video images of the shopping cart 210 during a timeof interest 250 during a transaction (or when the shopping cart 210 isnear the point of sale terminal 230 or a scanner 240), the environment100 includes a video camera 220, placed above the transaction area 200.The video camera 220 records operator and customer activity in thetransaction area 200 and stores the recorded video images in a videorepository 170.

For example, as illustrated in FIG. 1, at 2 o'clock (i.e. the time ofinterest 250), the shopping cart 210 is involved in a transaction thatinvolves the purchase of an item 210-1. Since the item 210-1 has beenplaced on a conveyor belt, the video camera 220 records an image 170-1of the shopping cart 210 as being empty of any items.

In addition, as the item 210-1 is scanned by the operator of the pointof sale terminal 230, transaction data 160 is created. For example, thetransaction data can include the time of purchase, the time the item210-1 was scanned, information identifying the item 210-1, the itemprice, and total amount paid by the customer.

In order to create an indication of a suspicion level 180 for thepurchase of the item 210-1, the Cart Inspector 150 performs videoanalysis 150-1 of the video image 170-1 that portrays the shopping cart210 at 2 o'clock.

Since the video image 170-1 (i.e. the target image) shows that theshopping cart 210 was empty at 2 o'clock when it was in the transactionarea 200, it is likely that the item's price 210-1 was included in thetotal amount paid by the customer. The Cart Inspector 150 creates anindication of a low level of suspicion 180 for the transaction recordedin the video image 170-1. Thus, before reviewing the video image 170-1,the indication of the low level of suspicion 180 informs securitypersonnel that the transaction involving the item 210-1 most likely didnot result in a financial loss to the retail establishment.

Turning now to FIG. 2, FIG. 2 illustrates an example block diagram of acomputer system configured with a Cart Inspector 150 creating a lowsuspicion level 180 for a transaction according to embodiments herein.

During video analysis 150-1, the Cart Inspector 150-1 obtains a targetimage. The target image can be a video image 170-1 that shows theshopping cart 210 was empty at 2 o'clock when it was in the transactionarea 200. In addition, the Cart Inspector 150-1 obtains a referencerepresentation, which can be a predefined image of an empty cart 150-3.

The Cart Inspector 150 performs an image comparison function 150-2 tocompare both images 170-1, 150-3. Since both the images 170-1, 150-3depict an empty shopping cart, the result 150-2-1 of the imagecomparison function 150-2 is a detection of a similarity between theimages 170-1, 150-3. The similarity between the images 170-1, 150-3indicates that the transaction recorded in the video image 170-1 thatshows the shopping cart 210 was empty at 2 o'clock was most likely anon-suspicious transaction. Based on the video analysis 150-1 of theimage 170-1, the Cart Inspector 150 creates an indication of a lowsuspicion level 180 for the transaction (i.e. the purchase of the item210-1 and 2 o'clock).

Referring now to FIG. 3, FIG. 3 is an example block diagram of acomputer system configured with a Cart Inspector 150 classifying ashopping cart item 210-2, which is portrayed in images 170-2, 170-2-1 ofa shopping cart 210, as a moving item according to embodiments herein.

During video analysis 150-1, the Cart Inspector 150-1 obtains a targetimage, such as a video image 170-2 of a non-empty shopping cart. Inaddition, the Cart Inspector 150-1 obtains a reference representationsuch as a predefined image of an empty cart 150-3.

The Cart Inspector 150 performs an image comparison function 150-2 tocompare both images 170-2, 150-3. Since the image 170-2 is that of anon-empty shopping cart, the result 150-2-2 of the image comparisonfunction 150-2 is a detection of a difference between the images 170-2,150-3. The difference between the images 170-2, 150-3 indicates that thetransaction recorded in the video image 170-2 may be either an image ofa suspicious transaction or an image of a false positive condition.

To determine whether the image 170-2 is an image of a moving itempresent in a shopping cart (i.e. a false positive condition), the videoanalysis 150-1 includes a motion detection function 150-4. The motiondetection function 150-4 identifies a portion 171 of the non-emptyshopping cart image 170-2 which corresponds with the item 210-2. Inaddition, the Cart Inspector 150 identifies another video image 170-2-1of the same transaction from the video repository 170. For example, avideo image 170-2-1 taken a few seconds later (or earlier) can beobtained by the Cart Inspector 150. The Cart Inspector 150 furtheridentifies a portion 172 of the later image 170-2-1 which correspondswith the item 210-2.

The motion detection function 150-4 processes the two portions 171, 172in order to identify a motion-based variation between the images'pixels. If the item 210-2 was moving when the images 170-2, 170-2-1 werecreated, the motion detection function 150-4 results 150-4-1 in adetection of the pixel differences.

Based on the result 150-2-1 of the image compare function 150-2 and theresult 150-4-1 of the motion detection function 150-4, an itemclassifier 150-5 classifies the item 210-2 as a non-store item, such asa moving item (e.g. a child, a pet). Since presence of a moving item ina shopping cart is a non-suspicious event, the Cart Inspector 150 lowersthe suspicion level 300 for the transaction with respect to the item's210-2 presence in the shopping cart.

In another embodiment, the Cart Inspector 150 detects a moving item byapplying a time-based recurrent motion measurement. The Cart Inspector150 uses an item silhouette and a cart silhouette from the non-emptycart image 170-2, and an amount of time to compute a motion value foreach pixel in the total item silhouette.

Regarding FIG. 4, FIG. 4 is an example block diagram of a computersystem configured with a Cart Inspector classifying a shopping cart item210-3, which is portrayed in an image 170-3 of a shopping cart, as abagged item according to embodiments herein. It is understood that theshopping cart item 210-3 can be located anywhere in the shopping cartand need not be in the basket area of the shopping cart for the CartInspector 150 to classify the shopping cart item 210-3. For example, theshopping cart item can be situated beneath the basket area of theshopping cart and the Cart Inspector 210-3 will classify the shoppingcart item 210-3 as well.

During video analysis 150-1, the Cart Inspector 150-1 obtains a targetimage, such as, for example, a video image 170-3 of a non-empty shoppingcart. In addition, the Cart Inspector 150-1 obtains a referencerepresentation, such as a predefined image of an empty cart 150-3.

The Cart Inspector 150 performs an image comparison function 150-2 tocompare both images 170-3, 150-3. Since the image 170-3 is that of anon-empty shopping cart, the result 150-2-3 of the image comparisonfunction 150-2 is a detection of a difference between the images 170-3,150-3. The difference between the images 170-3, 150-3 indicates that thetransaction recorded in the video image 170-3 may be either an image ofa suspicious transaction or an image of a false positive condition.

To determine whether the image 170-3 is an image of a bagged itempresent in a shopping cart (i.e. a false positive condition), the videoanalysis 150-1 includes a color detection function 150-6. The colordetection function 150-6 processes pixels from a portion 173 of theimage 170-3 that corresponds with the item 210-3 in the shopping cart.

For example, in one embodiment, the color detection function 150-6 usesa color model on a precompiled set of bag samples. Since store bags areconstant and are uniform in color, a probability distribution function(PDF) can be computed for each bag type. A bag confidence level can begenerated for each pixel in the portion 173 (e.g. a total itemsilhouette) by using the PDF to calculate a likelihood that it is a bagpixel.

When the color detection function 150-6 results 150-6-1 in detecting thecolor of the shopping bag in the portion 173 of the image 170-3 of thenon-empty shopping cart, the item classifier 150-5 classifies the item210-3 as a bagged item. Since presence of a bagged item in a shoppingcart is a non-suspicious event, the Cart Inspector 150 lowers thesuspicion level 400 for the transaction with respect to the item's 210-3presence in the shopping cart.

Turning now to FIG. 5, FIG. 5 is an example block diagram of a computersystem configured with a Cart Inspector 150 classifying a shopping cartitem 210-4 as a bulk item according to embodiments herein.

During video analysis 150-1, the Cart Inspector 150-1 obtains a videoimage 170-4 of a non-empty shopping cart. In addition, the CartInspector 150-1 obtains a predefined image of an empty cart 150-3.

The Cart Inspector 150 performs an image comparison function 150-2 tocompare both images 170-4, 150-3. Since the image 170-4 is that of anon-empty shopping cart, the result 150-2-4 of the image comparisonfunction 150-2 is a detection of a difference between the images 170-4,150-3. The difference between the images 170-4, 150-3 indicates that thetransaction recorded in the video image 170-4 may be either an image ofa suspicious transaction or an image of a false positive condition.

To determine whether the image 170-4 is an image of a bulk item presentin a shopping cart (i.e. a false positive condition), the Cart Inspector150 performs data analysis 150-7 on transaction data 160. The CartInspector 150 obtains a predefined list of bulk items 150-7-1 along withthe transaction data 160. The predefined list of bulk items 150-7-1describes items that are commonly left in shopping carts during atransaction.

A data compare function 150-8 searches the transaction data 160 forinformation related to any bulk item listed in the predefined list ofbulk items 150-7-1. When the data compare function finds such bulk iteminformation in the transaction data 160, the data compare function 150-8creates a result 150-8-1 indicating the presence of such bulk iteminformation.

Based on the result, 150-8-1, the item classifier 150-5 classifies theitem 210-4 as a bulk item. Since presence of a bulk item in a shoppingcart is a non-suspicious event, the Cart Inspector 150 lowers thesuspicion level 500 for the transaction with respect to the item's 210-4presence in the shopping cart.

In another embodiment, for large, heavy, or awkward items, it is commonfor the operator of a point of sale terminal 230 to scan the item 210-4with a hand scanner or enter the item's identification number into thepoint of sale terminal 230 by hand. To detect bulk items, the CartInspector 150 obtains a precompiled item library (including imagetemplates, features, item visual representations, etc.). This librarydefines those large, heavy, or awkward items that are customarily leftin shopping carts by customers during a transaction.

The Cart Inspector 150 defines a segment of the image 170-4 of thenon-empty cart. The segment includes the representation of the item210-4 portrayed in the image 170-4 of the non-empty cart.

When a transaction completes, all of the item information from thetransaction data 160 is obtained. The precompiled item library is thenqueried for visual representations (e.g. images, templates, geometricproperty information) of each item described in the transaction data160.

The Cart Inspector 250 compares the visual representation of each itemdescribed in the transaction data with the segment that includes therepresentation of the item 210-4 portrayed in the image 170-4 of thenon-empty cart. If the segment correlates with any of the visualrepresentations of the items described in the transaction data 160, thenthe suspicion level for the transaction is decreased with respect theitem 210-4. However, if the segment fails to correlate with any of thevisual representations of the items described in the transaction data160, then the suspicion level for the transaction is increased withrespect the item 210-4.

There are many methods available for image comparison includinghistogram color analysis, geometric analysis, and edge comparisonanalysis. One embodiment employs the use of a multi-resolutioncorrelation technique. The images in the database are transformed into apyramid image using a wavelet transform. A correlation score is computedand a match is determined by comparing against a confidence threshold.Those items that have no matches are considered suspicious.

FIG. 6 is an example block diagram of a computer system configured witha Cart Inspector 150 classifying a shopping cart item, which isportrayed in an image 170-6 of a shopping cart, as a suspicious itemaccording to embodiments herein.

The Cart Inspector 150 performs an image comparison function 150-2 tocompare both images 170-6, 150-3. Since the image 170-6 is that of anon-empty shopping cart, the result 150-2-5 of the image comparisonfunction 150-2 is a detection of a difference between the images 170-6,150-3. The difference between the images 170-6, 150-3 indicates that thetransaction recorded in the video image 170-6 may be either an image ofa suspicious transaction or an image of a false positive condition.Thus, if the Cart Inspector 150 detects that no false positive conditionexists, then the image 170-6 of the non-empty cart most likely is arecording of a suspicious transaction.

To determine whether the image 170-6 is an image of a moving itempresent in a shopping cart (i.e. a false positive condition), the videoanalysis 150-1 performs the motion detection function 150-4. If the itemwas moving when it was in the shopping cart, the motion detectionfunction 150-4 results in a detection of the pixel differences (asdiscussed above with regard to FIG. 3). However, based on video analysis150-1 involving the image 170-6 of the non-empty shopping cart, theresults 150-4-2 of the motion detection function 150-4 fails to detectpixel difference. Thus, the item in the shopping cart portrayed in theimage 170-6 is most likely not a moving item.

To determine whether the image 170-6 is an image of a bagged itempresent in a shopping cart (i.e. a false positive condition), the videoanalysis 150-1 performs the color detection function 150-6. The colordetection function 150-6 processes pixels from a portion of the image170-6 that corresponds with the item in the shopping cart. Based onvideo analysis 150-1, the results 150-6-2 of the color detectionfunction 150-6 fails to detect a distribution of color correspondingwith a shopping bag. Thus, the item in the shopping cart portrayed inthe image 170-6 is most likely not a bagged item.

To determine whether the image 170-6 is an image of a bulk item presentin a shopping cart (i.e. a false positive condition), the Cart Inspector150 performs data analysis 150-7 on transaction data 160. The datacompare function 150-8 searches the transaction data 160 for informationrelated to any bulk item listed in the predefined list of bulk items150-7-1. When the data compare function 150-8 fails to find bulk iteminformation in the transaction data 160, the data compare function 150-8creates a result 150-8-2 indicating that there is no bulk iteminformation in the transaction data 160.

Since the item portrayed in the image 170-6 as present in the shoppingcart is not a moving item, a bulk item, or a bagged item, it is highlylikely that the item was never placed on the conveyor belt and/orscanned by the operator of the point of sale terminal 230. Thus, thereis a probability that the item's price was not included in the totalprice paid by the customer. The item classifier 150-5 thereby classifiesthe item as a suspicious item 150-5-5 which increases the suspicionlevel 700 for the transaction with respect to the item portrayed in theimage 170-6 as present in the shopping cart.

FIG. 7 is an example block diagram illustrating an architecture of acomputer system 110 that executes, runs, interprets, operates orotherwise performs a Cart Inspector application 150-10 and/or CartInspector process 150-11 (e.g. an executing version of a Cart Inspector150 as controlled or configured by user 108) according to embodimentsherein.

Note that the computer system 110 may be any type of computerized devicesuch as a personal computer, a client computer system, workstation,portable computing device, console, laptop, network terminal, etc. Thislist is not exhaustive and is provided as an example of differentpossible embodiments.

In addition to a single computer embodiment, computer system 110 caninclude any number of computer systems in a network environment to carrythe embodiments as described herein.

As shown in the present example, the computer system 110 includes aninterconnection mechanism 111 such as a data bus, motherboard or othercircuitry that couples a memory system 112, a processor 113, aninput/output interface 114, and a display 130. If so configured, thedisplay can be used to present a graphical user interface of the CartInspector 150 to user 108. An input device 116 (e.g., one or moreuser/developer controlled devices such as a keyboard, mouse, touch pad,etc.) couples to the computer system 110 and processor 113 through aninput/output (I/O) interface 114. The computer system 110 can be aclient system and/or a server system. As mentioned above, depending onthe embodiment, the Cart Inspector application 150-10 and/or the CartInspector process 150-11 can be distributed and executed in multiplenodes in a computer network environment or performed locally on a singlecomputer.

During operation of the computer system 110, the processor 113 accessesthe memory system 112 via the interconnect 111 in order to launch, run,execute, interpret or otherwise perform the logic instructions of theCart Inspector application 150-1. Execution of the Cart Inspectorapplication 150-10 in this manner produces the Cart Inspector process150-2. In other words, the Cart Inspector process 150-11 represents oneor more portions or runtime instances of the Cart Inspector application150-10 (or the entire application 150-1) performing or executing withinor upon the processor 113 in the computerized device 110 at runtime.

The Cart Inspector application 150-10 may be stored on a computerreadable medium (such as a floppy disk), hard disk, electronic,magnetic, optical, or other computer readable medium. It is understoodthat embodiments and techniques discussed herein are well suited forother applications as well.

Those skilled in the art will understand that the computer system 110may include other processes and/or software and hardware components,such as an operating system. Display 130 need not be coupled directly tocomputer system 110. For example, the Cart Inspector application 150-10can be executed on a remotely accessible computerized device via thecommunication interface 115.

Regarding the flowcharts 900, 1000, 1100, 1200 1300 and 1400, FIG. 8through FIG. 13 illustrate various embodiment of the Cart Inspector 150.The rectangular elements in flowcharts 900, 1000, 1100, 1200,1300 and1400 represent “processing blocks” and represent computer softwareinstructions or groups of instructions upon a computer readable medium.Additionally, the processing blocks represent steps performed byhardware such as a computer, digital signal processor circuit,application specific integrated circuit (ASIC), etc.

Flowcharts 900, 1000, 1100, 1200, 1300 and 1400 do not necessarilydepict the syntax of any particular programming language. Rather,flowcharts 900, 1000, 1100, 1200, 1300 and 1400 illustrate thefunctional information one of ordinary skill in the art requires tofabricate circuits or to generate computer software to perform theprocessing required in accordance with the present invention. It will beappreciated by those of ordinary skill in the art that unless otherwiseindicated herein, the particular sequence of steps described isillustrative only and may be varied without departing from the spirit ofthe invention. Thus, unless otherwise stated, the steps described beloware unordered, meaning that, when possible, the steps may be performedin any convenient or desirable order.

FIG. 8 is an example flowchart 900 of processing steps performed by theCart Inspector 150 to create an indication of a suspicion level 180 fora transaction according to embodiments herein.

At step 910, the Cart Inspector 150 obtains video data associated with atime of interest 250. The video data originates from a video camera 220that monitors a transaction area 200.

At step 920, the Cart Inspector 150 analyzes the video data 170-1 withrespect to an image of a cart 170-1 involved in a transaction in thetransaction area 200 during the time of interest 250. The video dataincludes the image of the cart 170-1.

At step 930, the Cart Inspector 150 creates an indication of a suspicionlevel 180 for the transaction based on analysis of the one image of thecart 170-1 provided in the video data.

FIG. 9 is an example flowchart 1000 of processing steps performed by theCart Inspector to obtain video data associated with a time of interest250 according to embodiments herein.

At step 1010, the Cart Inspector 150 identifies a time stamp intransaction data 160 of a transaction.

At step 1020, the Cart Inspector 150 identifies the time stamp as thelast time stamp that appears in the transaction data 160.

At step 1030, the Cart Inspector 150 defines the one time of interest250 as contemporaneous with the time stamp.

At step 1040, the Cart Inspector 150 identifies a portion(s) of thevideo data created by the video camera 220 during the time of interest250 which contain an image(s) of the cart 170-1 in the transaction areaduring time of interest 250.

In another embodiment, the Cart Inspector 150 defines a criticallocation in the transaction area 200, such as the location of a scanningdevice 240 or the point of sale terminal 230.

The Cart Inspector 150 defines the time of interest 250 as when the cartis present at (or proximate to) the critical location (e.g. the scanningdevice 240, point of sale terminal 230) in the transaction area 200.

The Cart Inspector 150 identifies a portion of the video data, createdby the video camera 220 during the time of interest 250, which containsan image 170-1 of the cart at the critical location in the transactionarea 200 during the time of interest 250.

FIGS. 10-11 are example flowcharts 1100, 1200 of processing stepsperformed by the Cart Inspector 150 to compare an image of a shoppingcart 170-1 with a predefined image of an empty shopping cart 150-3according to embodiments herein.

At step 1110, the Cart Inspector 150 performs a comparison of the imageof the cart 170-1 with a predefined image of an empty cart 150-3 todetermine whether the image of the cart 170-1 portrays an empty cart.

At step 1120, if the comparison results in detection of a similaritybetween the two images 170-1, 150-3, the Cart Inspector 150 sets theindication of the suspicion level to a lowest suspicion level 180.

As illustrated in FIG. 11, at step 1130, if the comparison results indetection of a difference between the two images 170-1, 150-3, the CartInspector 150 performs steps 1140-1160 for each portion of the image ofthe cart 170-1 that portrays an item(s) in the cart:

At step 1140, the Cart Inspector 150 determines whether the item (e.g.item 210-2, 210-3, 210-4 or 210-5) in the cart qualifies for aclassification.

At step 1150, in response to the item (e.g. item 210-2, 210-3, 210-4 or210-5) in the cart qualifying for a classification, the Cart Inspector150 decreases the suspicion level 300, 400, 500 for the transaction.

At step 1160, in response to the item (e.g. item 210-2, 210-3, 210-4 or210-5) in the cart failing to qualify for any classification, the CartInspector 150 increases the suspicion level for the transaction.

FIG. 12 is an example flowchart 1300 of processing steps performed bythe Cart Inspector 150 to determine whether a shopping cart item (e.g.item 210-2, 210-3, 210-4 or 210-5) portrayed in an image of a shoppingcart qualifies as a moving item, a bagged item, an observable item or abulk item according to embodiments herein.

To process an occurrence of a first false positive condition, at step1310, the Cart Inspector 150 detects an indication of movement over timein a portions 171, 172 of images of the cart 170-2, 170-2-1 that portraya item 210-2 in the cart.

At step 1320, upon detecting the indication of movement, the CartInspector 150 classifies the item 210-2 in the cart as a non-store item150-5-1, such a moving item (e.g. a child, a pet).

To process an occurrence of a second false positive condition, at step1330, the Cart Inspector 150 detects a distribution of a color in aportion 173 of an image of the cart 170-3 that portrays the item 210-3in the cart. The detected color corresponds to a shopping bag used inthe transaction area 200.

At step 1340, upon detecting the distribution of the color, the CartInspector 150 classifies the item 210-3 in the cart as a bagged item150-5-2.

To process an occurrence of a third false positive condition, at step1350, the Cart Inspector 150 receives a signal that detected a placementof an item 210-5 in a region within the cart during the time of interest250. The placement of the item 210-5 signifies a likelihood that theitem 210-5 is not involved in a suspicious transaction.

At step 1360, upon receipt of the signal, the Cart Inspector 150classifies the item as an observable item 150-5-4.

To process an occurrence of a fourth false positive condition, at step1370, the Cart Inspector 150 classifies the item 210-4 in the cart as abulk item 150-5-3 upon detecting information in the transaction data 160that is related to a predefined bulk item. In another embodiment, theCart Inspector 150 obtains video data associated with a time of interest250. The video data originates a video camera(s) 220 that monitors atransaction area 200. For example, the video camera(s) 220 can beelevated over a horizontal plane where the transaction occurs in thetransaction area 220, such that the video camera(s) 220 recordtransactions in the transaction area 220 for a vantage point above thetransaction area 200.

FIG. 13 is an example flowchart 1400 of processing steps performed bythe Cart Inspector 150 to create a minimum suspicion level thatrepresents a least suspicious state of the transaction according toembodiments herein.

At step 1410, the Cart Inspector 150 obtains video data from a videocamera(s) 220 that monitors a transaction area 200.

At step 1420, the Cart Inspector 150 analyzes a plurality of targetimages in the video data associated with a transaction in thetransaction area 200. Thus, the Cart Inspector 150 analyzes each videoframe created by the camera 200 during the transaction.

At step 1430, based on analysis of each of the plurality of targetimages, the Cart Inspector 150 identifies a portion of the plurality oftarget images that represent a least suspicious state of thetransaction.

At step 1440, the Cart Inspector 150 identifies at least oneleast-suspicious image that represents a time of interest during thetransaction that a cart 210 is least likely to contain unpurchasedmerchandise items.

At step 1440, the Cart Inspector 150 creates a minimum suspicion levelthat represents the least suspicious state of the transaction.

For example, the Cart Inspector 150 performs the video analysis asdiscussed throughout this document upon each video frame created for thetransaction. By doing so, a suspicion level is created for each videoframe of the transaction. Hence, one of the video frames will have alowest suspicion level as compared to the other video frames of thetransaction. The Cart Inspector 150 identifies the video frame with thelowest suspicion level as a least-suspicious image of that transactionbecause the “lowest suspicion level” assigned to that video framerepresents a point in time (or a location in the transaction area) wherethe transaction was at its least suspicious state.

Note again that techniques herein are well suited for a Cart Inspector150 that performs video analysis 150-1 of target images 170-1, 170-2,170-3, 170-5, 170-6 that portray a transaction near a point of saleterminal 230. Based on the video analysis 150-1, a suspicion level 300,400, 500, 600, 700 for the transaction is created when the target image170-1, 170-2, 170-3, 170-5, 170-6 portrays an item(s) 210-2, 210-3,210-4, 210-5, 210-6 transported through a transaction area 200 at aparticular time 250 during the transaction.

The methods and systems described herein are not limited to a particularhardware or software configuration, and may find applicability in manycomputing or processing environments. The methods and systems may beimplemented in hardware or software, or a combination of hardware andsoftware. The methods and systems may be implemented in one or morecomputer programs, where a computer program may be understood to includeone or more processor executable instructions. The computer program(s)may execute on one or more programmable processors, and may be stored onone or more storage medium readable by the processor (including volatileand non-volatile memory and/or storage elements), one or more inputdevices, and/or one or more output devices. The processor thus mayaccess one or more input devices to obtain input data, and may accessone or more output devices to communicate output data. The input and/oroutput devices may include one or more of the following: Random AccessMemory (RAM), Redundant Array of Independent Disks (RAID), floppy drive,CD, DVD, magnetic disk, internal hard drive, external hard drive, memorystick, or other storage device capable of being accessed by a processoras provided herein, where such aforementioned examples are notexhaustive, and are for illustration and not limitation.

The computer program(s) may be implemented using one or more high levelprocedural or object-oriented programming languages to communicate witha computer system; however, the program(s) may be implemented inassembly or machine language, if desired. The language may be compiledor interpreted.

As provided herein, the processor(s) may thus be embedded in one or moredevices that may be operated independently or together in a networkedenvironment, where the network may include, for example, a Local AreaNetwork (LAN), wide area network (WAN), and/or may include an intranetand/or the Internet and/or another network. The network(s) may be wiredor wireless or a combination thereof and may use one or morecommunications protocols to facilitate communications between thedifferent processors. The processors may be configured for distributedprocessing and may utilize, in some embodiments, a client-server modelas needed. Accordingly, the methods and systems may utilize multipleprocessors and/or processor devices, and the processor instructions maybe divided amongst such single- or multiple-processor/devices.

The device(s) or computer systems that integrate with the processor(s)may include, for example, a personal computer(s), workstation(s) (e.g.,Sun, HP), personal digital assistant(s) (PDA(s)), handheld device(s)such as cellular telephone(s), laptop(s), handheld computer(s), oranother device(s) capable of being integrated with a processor(s) thatmay operate as provided herein. Accordingly, the devices provided hereinare not exhaustive and are provided for illustration and not limitation.

References to “a processor”, or “the processor,” may be understood toinclude one or more microprocessors that may communicate in astand-alone and/or a distributed environment(s), and may thus beconfigured to communicate via wired or wireless communications withother processors, where such one or more processor may be configured tooperate on one or more processor-controlled devices that may be similaror different devices. Use of such “processor” terminology may thus alsobe understood to include a central processing unit, an arithmetic logicunit, an application-specific integrated circuit (IC), and/or a taskengine, with such examples provided for illustration and not limitation.

Furthermore, references to memory, unless otherwise specified, mayinclude one or more processor-readable and accessible memory elementsand/or components that may be internal to the processor-controlleddevice, external to the processor-controlled device, and/or may beaccessed via a wired or wireless network using a variety ofcommunications protocols, and unless otherwise specified, may bearranged to include a combination of external and internal memorydevices, where such memory may be contiguous and/or partitioned based onthe application.

Throughout the entirety of the present disclosure, use of the articles“a” or “an” to modify a noun may be understood to be used forconvenience and to include one, or more than one of the modified noun,unless otherwise specifically stated.

Elements, components, modules, and/or parts thereof that are describedand/or otherwise portrayed through the figures to communicate with, beassociated with, and/or be based on, something else, may be understoodto so communicate, be associated with, and or be based on in a directand/or indirect manner, unless otherwise stipulated herein.

Although the methods and systems have been described relative to aspecific embodiment thereof, they are not so limited. Obviously manymodifications and variations may become apparent in light of the aboveteachings. Many additional changes in the details, materials, andarrangement of parts, herein described and illustrated, may be made bythose skilled in the art.

What is claimed is:
 1. A method comprising: obtaining video dataassociated with at least one time of interest, the video dataoriginating from at least one video camera that monitors a transactionarea; analyzing the video data with respect to at least one target imageassociated with a transaction in the transaction area during the atleast one time of interest, the at least one target image provided inthe obtained video data; and creating an indication of a suspicion levelfor the transaction based on analysis of the at least one target image.2. The method as in claim 1, wherein obtaining video data associatedwith the at least one time of interest includes: identifying a timestamp in transaction data of the transaction; defining the at least onetime of interest as contemporaneous with the time stamp; and identifyingthe at least one target image as included in at least one portion of thevideo data created during the at least one time of interest.
 3. Themethod as in claim 2, wherein identifying the time stamp includes:identifying a last time stamp in the transaction data.
 4. The method asin claim 1, wherein obtaining video data associated with the at leastone time of interest includes: defining at least one critical locationin the transaction area; defining the at least one time of interest aswhen a cart is the critical location in the transaction area, the cartcomprising a device for transporting items through a retail environment;and identifying the at least one target image as included in at leastone portion of the video data created during the at least one time ofinterest, the at least one target image thereby portraying activityoccurring at the critical location during the at least one time ofinterest.
 5. The method as in claim 4, wherein defining the criticallocation in the transaction area includes: defining the criticallocation as a location in the transaction area suited fordifferentiating between suspicious activity and non-suspicious activity.6. The method as in claim 4, wherein defining the critical location inthe transaction area includes: defining the critical location as alocation where the cart is in the transaction area and most likely to beempty when it is involved in a non-suspicious transaction.
 7. The methodas in claim 1, wherein analyzing the video data with respect to at leastone target image associated with a transaction includes: performing acomparison of the at least one target image with at least one referencerepresentation to determine the extent that the at least one targetimage portrays one of imagery of non-suspicious activity and imagery ofsuspicious activity.
 8. The method as in claim 7, wherein performing thecomparison of the at least one target image with the at least onereference representation includes: defining the at least one referencerepresentation as a template depicting a non-suspicious transaction. 9.The method as in claim 7, wherein performing the comparison of the atleast one target image with the at least one reference representationincludes: comparing at least one target image depicting a cart proximateto a point of sale location in the transaction area with the at leastone reference representation, the at least one reference representationcomprising a template of an empty cart, the cart suitable fortransporting items through the transaction area.
 10. The method as inclaim 7, wherein creating the indication of the suspicion level for thetransaction based on analysis of the at least one target image includes:if the comparison results in detection of a similarity between the atleast one target image and the at least one reference representation,setting the indication of the suspicion level to a lowest suspicionlevel.
 11. The method as in claim 7, wherein creating the indication ofthe suspicion level for the transaction based on analysis of the atleast one target image includes: if the comparison results in detectionof a difference between the at least one target image and the at leastone reference representation, for each portion of the at least onetarget image that portrays at least one item transported through thetransaction area: adjusting the suspicion level of the transaction basedon at least one of (i) a location of the at least one item in the cart;(ii) a size of the at least one item; (iii) a shape of the at least oneitem; (iv) a color of the at least one item; (v) a characteristic of theat least one item; (vi) a probability that the item qualifies for afalse positive classification.
 12. The method as in claim 11, whereinadjusting the suspicion level of the transaction based on the locationof the at least one item in the cart includes one of: minimallyincreasing the suspicion level when the portion of the at least onetarget image portrays at least one item in an upper compartment of thecart; moderately increasing the suspicion level when the portion of theat least one target image portrays at least one item in a basket of thecart; and substantially increasing the suspicion level when the portionof the at least one target image portrays at least one item in beneaththe a basket of the cart.
 13. The method as in claim 11, whereinadjusting the suspicion level of the transaction based on theprobability of the item qualifying for the false positive classificationincludes: detecting an indication of movement over time in the portionof the at least one target image that portrays the item transportedthrough the transaction area; and upon detecting the indication ofmovement, creating an indication that the item transported through thetransaction area is likely a moving item.
 14. The method as in claim 11,wherein adjusting the suspicion level of the transaction based on theprobability of the item qualifying for the false positive classificationincludes: detecting a distribution of a color in the portion of the atleast one target image that portrays the item transported through thetransaction area, the color corresponding to a shopping bag used in thetransaction area; and upon detecting the distribution of the color,creating an indication that the item transported through the transactionarea is likely a bagged item.
 15. The method as in claim 11, whereinadjusting the suspicion level of the transaction based on theprobability of the item qualifying for the false positive classificationincludes: searching for an occurrence of at least one predefined bulkitem the transaction data for information related to the identificationof the at least one predefined bulk item; and upon detecting informationin the transaction data that is related to the at least one predefinedbulk item, creating an indication that the at least one item transportedthrough the transaction area is likely a bulk item.
 16. The method as inclaim 1, wherein analyzing the video data with respect to at least onetarget image associated with a transaction includes: identifying asegment of the video data that portrays at least one image of a cart, aportion of the at least one image of the cart portraying an itemtransported by the cart; obtaining a visual representation for each itemlisted in transaction data; comparing the portion of the at least oneimage of the cart portraying an item transported by the cart with thevisual representation for each item listed in transaction data; whereincreating an indication of a suspicion level includes: increasing thesuspicion level if the portion of the at least one image of the cartportraying an item transported by the cart fails to correlate with anyvisual representation for each item listed in transaction data; anddecreasing the suspicion level if the portion of the at least one imageof the cart portraying an item transported by the cart correlates withat least one of the visual representation for each item listed intransaction data.
 17. The method as in claim 1, further comprising:performing the step of obtaining the video data associated with a secondtime of interest and performing the step of analyzing the video datawith respect to at least one second target image associated with asecond transaction; performing the step of creating the indication of asuspicion level to create a second indication level for the secondtransaction based on analysis of the at least one second target image;creating a ranking of the at least one target image and the at least onesecond target image according to suspicion level.
 18. The method as inclaim 1, further comprising: defining a threshold suspicion level;comparing the indication of the suspicion level for the transactionbased on analysis of the at least one target image with the thresholdsuspicion level; upon detecting the indication of the suspicion levelsurpasses the threshold suspicion level, creating a notificationassociated with the transaction.
 19. The method as in claim 18, whereincreating the notification associated with the transaction includes:creating a real-time notification contemporaneously with the transactionas the transaction occurs in at least one of a self-checkout transactionarea and an assisted checkout transaction area.
 20. The method as inclaim 1, wherein obtaining video data associated with the at least onetime of interest, the video data originating from the at least one videocamera that monitors a transaction area includes: obtaining video dataoriginating from at least one video camera elevated to a positionsituated over a horizontal plane where the transaction occurs in thetransaction area, the at least one video camera thereby creating videodata that represents at least one of: (i) an overhead view of a carttransporting at least one item through the transaction area; and (ii) anelevated perspective view of the cart transporting at least one itemthrough the transaction area.
 21. A computer system comprising: aprocessor; a memory unit that stores instructions associated with anapplication executed by the processor; and an interconnect coupling theprocessor and the memory unit, enabling the computer system to executethe application and perform operations of: obtaining video dataassociated with at least one time of interest, the video dataoriginating from at least one video camera that monitors a transactionarea; analyzing the video data with respect to at least one target imageassociated with a transaction in the transaction area during the atleast one time of interest, the at least one target image provided inthe obtained video data; and creating an indication of a suspicion levelfor the transaction based on analysis of the at least one target image.22. A method comprising: obtaining video data from at least one videocamera that monitors a transaction area; analyzing a plurality of targetimages in the video data, the plurality of target images associated witha transaction in the transaction area; based on analysis of each of theplurality of target images, identifying a portion of the plurality oftarget images that represent a least suspicious state of thetransaction; creating a minimum suspicion level that represents theleast suspicious state of the transaction.
 23. The method as in claim22, wherein identifying the portion of the plurality of target imagesthat represent the least suspicious state of the transaction, includes:identifying at least one least-suspicious image, the at least oneleast-suspicious image representing at least one time of interest duringthe transaction that a cart is least likely to contain unpurchasedmerchandise items.